Have I implemented this function correctly?

[naggafin]

Hi guys! I'm writing a function for an algorithmic trading bot. The function is derived from a white paper from Pauna Cristian which I've linked here. In essence it is supposed to determine price cyclicality. The basic algorithm is given on page 148 (although the PDF excerpt is only a few pages; nonetheless that is the page number displayed). I believe I have implemented this correctly using the pandas library, however my confusion is in the fact that the paper mentions the oscillation for this function should be between 0 and 100. For my implementation it is 0.0 through 1.0. This leaves me doubting whether or not I'm fully understanding how to implement what is shown. If anyone would like to give input I would certainly appreciate it! Code sample below:

def price_cyclicality_function(df: DataFrame, p1=20, p2=50, n=20, a=0.33) -> DataFrame:
	if p1 > p2:
		return df
	
	# Make a DataFrame with the same number of rows
	tmp = DataFrame(index=df.index)
	
	# Get calculate our moving averages using a function from talib
	tmp['Ma'] = ta.SMA(df, timeperiod=p1)
	tmp['ma'] = ta.SMA(df, timeperiod=p2)
	
	# Find min/max values for Ma - ma in a window size of n
	tmp['min'] = (tmp['Ma'] - tmp['ma']).rolling(n).min()
	tmp['max'] = (tmp['Ma'] - tmp['ma']).rolling(n).max()
	
	# Get the derivative
	tmp['delta'] = (
		( tmp['max'] - (tmp['Ma'] - tmp['ma']) )/
		( tmp['max'] - tmp['min'] )
	)
	
	# And finally calculate PCY
	tmp['pcy'] = 0.0
	for i in range(1, len(df)):
		tmp.at[i, 'pcy'] = a*(tmp.at[i, 'delta'] - tmp.at[i-1, 'pcy']) + tmp.at[i-1, 'pcy']
	
	return tmp